Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 7, 2018

Neural Based Lumped Element Model of Capacitive RF MEMS Switches

  • Tomislav Ćirić , Rohan Dhuri , Zlatica Marinković EMAIL logo , Olivera Pronić-Rančić , Vera Marković and Larissa Vietzorreck
From the journal Frequenz

Abstract

In this paper a lumped element model of RF MEMS capacitive switches which is scalable with the lateral dimensions of the bridge is proposed. The dependence of the elements of the model on the bridge dimensions is introduced by using one or more artificial neural networks to model the relationship between the bridge dimensions and the inductive and resistive elements of the lumped element model. The achieved results show that the developed models have a good accuracy over the whole considered range of the bridge dimension values.

Funding statement: This work was funded by the bilateral Serbian-German project 451-03-01038/2015-09/4 supported by the DAAD foundation and Serbian Ministry of Education, Science and Technological Development. The work was also supported by the projects TR-32052 and III-43102 of the Serbian Ministry of Education, Science and Technological Development.

Acknowledgement

Authors would like to thank FBK Trento, Thales Alenia Italy, CNR Rome and University of Perugia, Italy for providing RF MEMS.

References

[1] Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design. Norwood, MA: Artech House Inc., 2000.Search in Google Scholar

[2] C. Christodoulou and M. Gerogiopoulos, Applications of Neural Networks in Electromagnetics. Norwood, MA: Artech House Inc., 2000.Search in Google Scholar

[3] P. Burrascano, S. Fiori, and M. Mongiardo, “A rewiew of artificial neural network applications in microwave computer-aided design,” Int. J. RF Microw. Comput. Aided Eng., vol. 9, no. 3, pp. 158–174, 1999.10.1002/(SICI)1099-047X(199905)9:3<158::AID-MMCE3>3.0.CO;2-VSearch in Google Scholar

[4] J. E. Rayas-Sanchez, “EM-based optimization of microwave circuits using artificial neural networks: The state-of-the-art,” IEEE Trans. Microw. Theory Techn., vol. 52, no. 1, pp. 420–435, 2004.10.1109/TMTT.2003.820897Search in Google Scholar

[5] C. Narendra, M. V. Karikeyan, and A. Mittal, “A review on the use of soft computing methods for microwave design applications,” Frequenz, vol. 63, no. 1–2, pp. 24–31, 2009.10.1515/FREQ.2009.63.1-2.24Search in Google Scholar

[6] Z. Marinković and V. Marković, “Temperature dependent models of low-noise microwave transistors based on neural networks,” Int. J. RF Microw. Comput. Aided Eng., vol. 15, no. 6, pp. 567–577, 2005.10.1002/mmce.20102Search in Google Scholar

[7] Z. Marinković, G. Crupi, A. Caddemi, and V. Marković, “Comparison between analytical and neural approaches for multibias small signal modelling of microwave scaled FETs,” Microw. Opt.Techn. Lett., vol. 52, no. 10, pp. 2238–2244, 2010.10.1002/mop.25432Search in Google Scholar

[8] J. H. Kabir, Y. Cao, and Q. Zhang, “Advances of neural network modelling methods for RF/microwave applications,” Appl. Comput. Electromagn. Soc. J., vol. 25, no. 5, pp. 423–432, 2010.Search in Google Scholar

[9] H. Kabir, L. Zhang, M. Yu, P. Aaen, J. Wood, and Q. J. Zhang, “Smart modeling of microwave devices,” IEEE Microw. Mag., vol. 11, pp. 105–108, May 2010.10.1109/MMM.2010.936079Search in Google Scholar

[10] F. Florian and R. Weigel, “An efficient neural network based modeling method for automotive EMC simulation,” Frequenz, vol. 65, no. 9–10, pp. 267–271, 2011.10.1515/FREQ.2011.039Search in Google Scholar

[11] Z. Marinković, G. Crupi, D. Schreurs, A. Caddemi, and V. Marković, “Microwave FinFET modelling based on artificial neural networks including lossy silicon substrate,” Microel. Eng., vol. 88, no. 10, pp. 3158–3163, 2012.10.1016/j.mee.2011.06.019Search in Google Scholar

[12] M. Agatonović, Z. Marinković, and V. Marković, “Application of ANNs in evaluation of microwave pyramidal absorber performance,” Appl. Comput. Electromagn. Soc. J., vol. 27, no. 4, pp. 326–333, 2012.Search in Google Scholar

[13] F. Gunes, S. Nesil, and S. Demirel, “Design and analysis of minkowski reflect array antenna using 3-D CST microwave studio-based neural network model with particle swarm optimization,” Int. J. RF Microw. Comput.-Aided Eng., vol. 23, no. 2, pp. 272–284, March 2013.Search in Google Scholar

[14] R. Zhou, A. Nie, Q. Zhang, and Y. Cao, “Simulation optimization to microwave components using neural network,” Int. J. Numer. Model. Electron. Networks Devices Fields, vol. 27, no. 1, pp. 1–9, Jan/Feb 2014.10.1002/jnm.1877Search in Google Scholar

[15] Z. Marinković, G. Crupi, A. Caddemi, G. Avolio, A. Raffo, V. Marković, G. Vannini, and D. M. M.-P. Schreurs, “Neural approach for temperature-dependent modeling of GaN HEMTs,” Int. J. Numer. Model. Electron. Networks Devices Fields, vol. 28, no. 4, pp. 359–370, July/August 2015.10.1002/jnm.2011Search in Google Scholar

[16] V. Đorđević, Z. Marinković, V. Marković, and O. Pronić-Rančić, “Extraction of microwave FET noise wave temperatures by using a novel neural approach,” Int. J. Comput. Math. Electrical Electron. Eng. COMPEL, vol. 35,, no. 1, pp. 339–349, January 2016.10.1108/COMPEL-07-2015-0254Search in Google Scholar

[17] M. Gad-el-Hak, The MEMS Handbook. Boca Raton, FL: CRC Press, 2002.10.1115/1.1508147Search in Google Scholar

[18] G. M. Rebeiz, RF MEMS Theory, Design, and Technology. New York: Wiley, 2003.10.1002/0471225282Search in Google Scholar

[19] E. Hamad and A. Omar, “An improved two-dimensional coupled electrostatic-mechanical model for RF MEMS switches,” J. Micromech. Microeng., vol. 16, pp. 1424, 2006.10.1088/0960-1317/16/7/041Search in Google Scholar

[20] L. Vietzorreck, “EM modeling of RF MEMS,” in 7th Int. Conf. Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, EuroSime 2006, Como, Italy, April 24–26, 2006, pp. 1–4.Search in Google Scholar

[21] Z. J. Guo, N. E. McGruer, and G. G. Adams, “Modeling, simulation and measurement of the dynamic performance of an ohmic contact, electrostatically actuated RF MEMS switch,” J. Micromech. Microeng., vol. 17, pp. 1899–1909, 2007.10.1088/0960-1317/17/9/019Search in Google Scholar

[22] Z. Volker, C. Siegel, B. Schönlinner, et al., “Switchable microwave circuits using the EADS low-complexity RF-MEMS process,” Frequenz, vol. 61, no. 9–10, pp. 199–202, 2007.10.1515/FREQ.2007.61.9-10.199Search in Google Scholar

[23] F. Diaferia, F. Deborgies, S. Di Nardo, B. Espana, P. Farinelli, A. Lucibello, R. Marcelli, B. Margesin, F. Giacomozzi, L. Vietzorreck, and F. Vitulli, “Compact 12×12 switch matrix integrating RF MEMS switches in LTCC hermetic packages,” in 44th European Microwave Conference (EuMC), pp. 199–202, 2014.10.1109/EuMC.2014.6986404Search in Google Scholar

[24] A. Napieralski, C. Maj, M. Szermer, P. Zajac, W. Zabierowski, M. Napieralska, Ł. Starzak, M. Zubert, R. Kiełbik, P. Amrozik, Z. Ciota, R. Ritter, M. Kamiński, R. Kotas, P. Marciniak, B. Sakowicz, K. Grabowski, W. Sankowski, G. Jabłoński, D. Makowski, A. Mielczarek, M. Orlikowski, M. Jankowski, and P. Perek, “Recent research in VLSI, MEMS and power devices with practical application to the ITER and DREAM projects,” Facta Universitatis Ser. Electron. Energ., vol. 27, no. 4, pp. 561–588, 2014.10.2298/FUEE1404561NSearch in Google Scholar

[25] I. Jokić, M. Frantlović, Z. Đurić, and M. Dukić, “RF MEMS/NEMS resonators for wireless communication systems and adsorption-desorption phase noise,” Facta Universitatis Ser. Electron. Energ., vol. 28, no. 3, pp. 345–381, 2015.10.2298/FUEE1503345JSearch in Google Scholar

[26] J. Iannacci, R. Gaddi, and A. Gnudi, “A experimental validation of mixed electromechanical and electromagnetic modeling of RF-MEMS devices within a standard IC simulation environment,” J. Microelectromech. Syst., vol. 19, no. 3, pp. 526–537, 2010.10.1109/JMEMS.2010.2048417Search in Google Scholar

[27] http://www.coventor/mems-solutions/products/memsSearch in Google Scholar

[28] Y. Lee, Y. Park, F. Niu, and D. Filipovic, “Design and optimisation of one-port RF MEMS resonators and related integrated circuits using ANN-based macromodelling approach,” IEE Proc. Circuits Devices Syst., vol. 153, no. 5, pp. 480–488, October 2006.10.1049/ip-cds:20045211Search in Google Scholar

[29] J. Wang, L. Sun, and Y. Linag, “Accurate EC-ANN modeling for a RF-MEMS extended tuning range varactor,” in Asia Pacific Conf. on Postgraduate Research in Microelectronics and Electronics (PrimeAsia 2010), Sept. 2010, pp. 360–363.10.1109/PRIMEASIA.2010.5604889Search in Google Scholar

[30] Z. Marinković, T. Kim, V. Marković, M. Milijić, O. Pronić-Rančić, T. Ćirić, and L. Vietzorreck, “Artificial neural network based design of RF MEMS capacitive shunt switches,” Appl. Comput. Electromagn. Soc. (ACES) J., vol. 31, no. 7, pp. 756–764, July 2016.Search in Google Scholar

[31] Z. Marinković, V. Marković, T. Ćirić, L. Vietzorreck, and O. Pronić-Rančić, “Artifical neural networks in RF MEMS switch modelling,” Facta Universitatis Ser. Electron. Energ., vol. 29, no. 2, pp. 177–191, 2016.10.2298/FUEE1602177MSearch in Google Scholar

[32] T. Ćirić, Z. Marinković, O. Pronić-Rančić, V. Marković, and L. Vietzorreck, “ANN approach for modeling of mechanical characteristics of RF MEMS capacitive switches – an overview,” Microwave Rev., vol. 23, no. 1, pp. 25–34, June 2017.Search in Google Scholar

[33] S. DiNardo, P. Farinelli, F. Giacomozzi, G. Mannocchi, R. Marcelli, B. Margesin, P. Mezzanotte, V. Mulloni, P. Russer, R. Sorrentino, F. Vitulli, and L. Vietzorreck, “Broadband RF-MEMS based SPDT,” in Proc. European Microwave Conf. 2006, Manchester, Great Britain, September 2006.Search in Google Scholar

[34] Advanced Design System 2009. Santa Rosa, CA: Electronic design automation software system produced by Keysight EEsof EDA.Search in Google Scholar

Received: 2018-01-15
Published Online: 2018-04-07
Published in Print: 2018-11-27

© 2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 8.5.2024 from https://www.degruyter.com/document/doi/10.1515/freq-2018-0023/html
Scroll to top button